Package org.apache.commons.math3.distribution

Class BinomialDistribution

java.lang.Object
org.apache.commons.math3.distribution.AbstractIntegerDistribution
org.apache.commons.math3.distribution.BinomialDistribution
All Implemented Interfaces:
Serializable, IntegerDistribution
public class BinomialDistribution extends AbstractIntegerDistribution
Implementation of the binomial distribution.
See Also:

  • Constructor Details

    • BinomialDistribution

      public BinomialDistribution(int trials, double p)
      Create a binomial distribution with the given number of trials and probability of success.

      Note: this constructor will implicitly create an instance of Well19937c as random generator to be used for sampling only (see AbstractIntegerDistribution.sample() and AbstractIntegerDistribution.sample(int)). In case no sampling is needed for the created distribution, it is advised to pass null as random generator via the appropriate constructors to avoid the additional initialisation overhead.

      Parameters:
      trials - Number of trials.
      p - Probability of success.
      Throws:
      NotPositiveException - if trials < 0.
      OutOfRangeException - if p < 0 or p > 1.

    • BinomialDistribution

      public BinomialDistribution(RandomGenerator rng, int trials, double p)
      Creates a binomial distribution.
      Parameters:
      rng - Random number generator.
      trials - Number of trials.
      p - Probability of success.
      Throws:
      NotPositiveException - if trials < 0.
      OutOfRangeException - if p < 0 or p > 1.
      Since:
      3.1

  • Method Details

    • getNumberOfTrials

      public int getNumberOfTrials()
      Access the number of trials for this distribution.
      Returns:
      the number of trials.

    • getProbabilityOfSuccess

      public double getProbabilityOfSuccess()
      Access the probability of success for this distribution.
      Returns:
      the probability of success.

    • probability

      public double probability(int x)
      For a random variable X whose values are distributed according to this distribution, this method returns P(X = x). In other words, this method represents the probability mass function (PMF) for the distribution.
      Parameters:
      x - the point at which the PMF is evaluated
      Returns:
      the value of the probability mass function at x

    • logProbability

      public double logProbability(int x)
      For a random variable X whose values are distributed according to this distribution, this method returns log(P(X = x)), where log is the natural logarithm. In other words, this method represents the logarithm of the probability mass function (PMF) for the distribution. Note that due to the floating point precision and under/overflow issues, this method will for some distributions be more precise and faster than computing the logarithm of IntegerDistribution.probability(int).

      The default implementation simply computes the logarithm of probability(x).

      Overrides:
      logProbability in class AbstractIntegerDistribution
      Parameters:
      x - the point at which the PMF is evaluated
      Returns:
      the logarithm of the value of the probability mass function at x

    • cumulativeProbability

      public double cumulativeProbability(int x)
      For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x). In other words, this method represents the (cumulative) distribution function (CDF) for this distribution.
      Parameters:
      x - the point at which the CDF is evaluated
      Returns:
      the probability that a random variable with this distribution takes a value less than or equal to x

    • getNumericalMean

      public double getNumericalMean()
      Use this method to get the numerical value of the mean of this distribution. For n trials and probability parameter p, the mean is n * p.
      Returns:
      the mean or Double.NaN if it is not defined

    • getNumericalVariance

      public double getNumericalVariance()
      Use this method to get the numerical value of the variance of this distribution. For n trials and probability parameter p, the variance is n * p * (1 - p).
      Returns:
      the variance (possibly Double.POSITIVE_INFINITY or Double.NaN if it is not defined)

    • getSupportLowerBound

      public int getSupportLowerBound()
      Access the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0). In other words, this method must return

      inf {x in Z | P(X invalid input: '<'= x) > 0}.

      The lower bound of the support is always 0 except for the probability parameter p = 1.
      Returns:
      lower bound of the support (0 or the number of trials)

    • getSupportUpperBound

      public int getSupportUpperBound()
      Access the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1). In other words, this method must return

      inf {x in R | P(X invalid input: '<'= x) = 1}.

      The upper bound of the support is the number of trials except for the probability parameter p = 0.
      Returns:
      upper bound of the support (number of trials or 0)

    • isSupportConnected

      public boolean isSupportConnected()
      Use this method to get information about whether the support is connected, i.e. whether all integers between the lower and upper bound of the support are included in the support. The support of this distribution is connected.
      Returns:
      true